In this paper, we introduce a novel iterative motion tracking
framework that combines 3D tracking techniques with
motion retrieval for stabilizing markerless human motion
capturing. The basic idea is to start human tracking without
prior knowledge about the performed actions. The resulting
3D motion sequences, which may be corrupted due to tracking
errors, are locally classified according to available motion
categories. Depending on the classification result, a
retrieval system supplies suitable motion priors, which are
then used to regularize and stabilize the tracking in the next
iteration step. Experiments with the HumanEVA-II benchmark
show that tracking and classification are remarkably
improved after few iterations.